Software development in the Internet-era has grown increasingly complex. Software applications must not only handle and process complex calculations and tasks, but must be able to accurately and efficiently process a large variety of data types and data content.
Testing of software applications, as a result, has also become complex and difficult. With modern development methods, it is very difficult to comprehensively test software applications accurately and in a timely fashion within short testing development cycles that are generally available. Members of the testing team must manually build and execute testing phases that include quality assurance deliverable, design assurance, quality assurance design, quality assurance automation and quality assurance execution, each of which involves complex development steps and any of which could be significantly delayed during development. The manual design and execution of each phase of these tests are inefficient and still results in many errors. As many of the software development process software applications handle complex data sets, the short time frame to validate testing results of such data can result in a significant number of issues. Even after tests are designed and executed, it is often difficult to reuse the tests across projects, as tests are often customized for that specific application. As a result, tests are often not reusable and must be redesigned from the ground up where the dataset formats differ. In addition, while many prior art systems support testing functions, they are not extendible, customizable, or support API commands. Due to these limitations, test administrators must manually create, define and administer tests by manually requesting execution of the tests and manually define schemas, data sets, and other options. Furthermore, the test administrator, using these prior art systems, must resolve frequent issues that require manual intervention before next step of software development cycle can proceed. In addition, when new modules, schemas, or other options are introduced to an existing system, prior art solutions may require that the existing modules be rewritten.
In addition to the foregoing, prior art systems do not allow for data and control segregation for different tests. Although data may be stored in separate database or separate portions of a database, the data is still at risk of theft. For example, data and information may be intercepted between user devices and the systems and engines or components of the system. These systems are at risk for by man-in-the-middle attacks. In addition, many prior art systems do not allow for a single instance of the platform to be deployable for a whole enterprise. Furthermore, the prior art does not support the use of secured data communications and storage via private and public keys or authentication. In addition, these prior art systems are at risks for unauthorized access and control of test design and execution and do not allow data and control access to be restricted.
Accordingly, a solution is needed to overcome these and other deficiencies of the prior art.